? 基于运动几何的视频抖动检测
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Journal of Computer Science and Technology 2018, Vol. 33 Issue (3) :475-486    DOI: 10.1007/s11390-018-1832-5
Special Section of CVM 2018 << Previous Articles | Next Articles >>
基于运动几何的视频抖动检测
Xiao-Qun Wu, Member, CCF, Hai-Sheng Li, Member, CCF, Member, IEEE, Jian Cao, Member, CCF, Qiang Cai, Senior Member, CCF, Member, IEEE
School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China;Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University Beijing 100048, China
Geometry of Motion for Video Shakiness Detection
Xiao-Qun Wu, Member, CCF, Hai-Sheng Li, Member, CCF, Member, IEEE, Jian Cao, Member, CCF, Qiang Cai, Senior Member, CCF, Member, IEEE
School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China;Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University Beijing 100048, China

摘要
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摘要 本文提出了一种用于自动检测临时视频中的全局抖动的新算法。基于由帧间几何变换定义的运动学模型,每帧幅度通过运动几何来计算。受运动感知的启发,我们研究人类视觉系统感知到的抖动运动的明显幅度。然后,我们使用阈值对比策略对每帧幅度进行统计,以确定感知到的抖动的发生。为了测试检测的准确性,本文同时提供了以手工抖动标签为基础构建的视频剪辑的数据集。实验证明,该算法可以获得较好的检测精度,与数据集中视频的主观判断相一致。
关键词视频抖动   运动学模型   运动感知     
Abstract: This paper presents a novel algorithm for automatically detecting global shakiness in casual videos. Per-frame amplitude is computed by the geometry of motion, based on the kinematic model defined by inter-frame geometric transformations. Inspired by motion perception, we investigate the just-noticeable amplitude of shaky motion perceived by human visual system. Then, we use the thresholding contrast strategy on the statistics of per-frame amplitudes to determine the occurrence of perceived shakiness. For testing the detection accuracy, a dataset of video clips is constructed with manual shakiness label as the ground truth. The experiments demonstrate that our algorithm can obtain good detection accuracy that is in concordance with subjective judgement on the videos in the dataset.
KeywordsVideo shakiness   kinematic model   motion perception     
Received 2018-01-04;
本文基金:

This work was partially supported by the National Natural Science Foundation of China under Grant No. 61602015, the Open Funding Project of State Key Laboratory of Virtual Reality Technology and Systems at Beihang University under Grant No. BUAAVR-16KF-06, Beijing Natural Science Foundation under Grant No. 4162019, and the Research Foundation for Young Scholars of Beijing Technology and Business University.

通讯作者: Xiao-Wu Chen     Email: chen@buaa.edu.cn
About author: Xiao-Qun Wu is now a lecturer in the School of Computer and Information Engineering, Beijing Technology and Business University, Beijing. She received her B.S. and M.S. degrees in mathematics from Zhejiang University, Hangzhou, in 2007 and 2009, respectively, and her Ph.D. degree in computer science from Nanyang Technological University, Singapore, in 2014. Her research focuses on computer graphics, multimedia processing and applications.
引用本文:   
Xiao-Qun Wu, Hai-Sheng Li, Jian Cao, Qiang Cai.基于运动几何的视频抖动检测[J]  Journal of Computer Science and Technology , 2018,V33(3): 475-486
Xiao-Qun Wu, Hai-Sheng Li, Jian Cao, Qiang Cai.Geometry of Motion for Video Shakiness Detection[J]  Journal of Computer Science and Technology, 2018,V33(3): 475-486
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